The Effects of Two-Stage Fuel Injection on Dimethyl-ether (DME) Homogeneous Charge Compression Ignition Engine Combustion
Bibliographic record
Abstract
Two-stage injection strategy was studied in dimethyl-ether homogeneous charge compression ignition engine combustion. An early direct injection, main injection, was applied to form a premixed charge followed by the second injection after the start of heat release. Experiments were carried out in a single-cylinder direct-injection diesel engine equipped with a common-rail injection system, and the combustion performance and exhaust emissions were tested with the various second injection timings and quantities. Engine speed was 1200 rpm, and the load was fixed at 0.2 MPa IMEP. Main injection timing for homogeneous mixture was fixed at −80 CAD, and the fuel quantity was adjusted to the fixed load. Second injection quantity was varied from 1 to 5 mg, and the timing was selected according to the heat release rate of the HCCI combustion without second injection. From the results, two-stage injection strategy showed the possibility to overcome the disadvantages of HCCI such as high HC / CO emissions and difficult combustion phase control. Heat release rate and pressure rise rate were reduced with the second injection. The decreased premixed charge concentration by the second injection lowered the heat release from low temperature reaction and high temperature reaction. The second injection showed the separated heat release with the third peak in heat release rate curve as the fuel quantity exceeded 3 mg. This contributed to the power stroke enhancement, and fuel consumption reduction. Peak in-cylinder pressure was decreased as the second injection timing was retarded and the quantity was increased. The portion of the heat release after TDC was increased, and the pressure rise rate was reduced as the combustion phase was delayed and the burn duration was lengthened. However, the lowered bulk gas temperature with dropped in-cylinder pressure caused the CO emission level rise.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".